Method for spectrophotometric blood oxygenation monitoring

ABSTRACT

According to the present invention, a method and apparatus for non-invasively determining the blood oxygen saturation level within a subject&#39;s tissue is provided. The method comprises the steps of: a) providing a spectrophotometric sensor operable to transmit light into the subject&#39;s tissue, and to sense the light; b) detecting light after passage through the subject&#39;s tissue using the sensor, and producing initial signal data from the light sensed; c) calibrating the sensor to that particular subject using the initial signal data, thereby accounting for the specific physical characteristics of the particular subject&#39;s tissue being sensed; and d) using the calibrated sensor to determine the blood oxygen parameter value within the subject&#39;s tissue.

This application is a continuation of U.S. patent application Ser. No.14/549,135 filed Nov. 20, 2014, which is a continuation of U.S. patentapplication Ser. No. 13/793,964 filed Mar.11, 2013, which is acontinuation of U.S. patent application Ser. No. 11/914,074 filed Nov.9, 2007, which is a national stage application of PCT Patent Applicationno. PCT/US06/18082 filed May 10, 2006 which claims priority to U.S.Provisional Patent Application No. 60/680,192 filed May 12, 2005, thedisclosures of which are herein incorporated by reference.

This invention was made with Government support under Contract No.2R44NS045488-02 awarded by the Department of Health & Human Services.The Government has certain rights in the invention.

BACKGROUND OF THE INVENTION

1. Technical Field

This invention relates to methods for non-invasively determiningbiological tissue oxygenation in general, and to non-invasive methodsutilizing near-infrared spectroscopy (NIRS) techniques for determiningthe same in particular.

2. Background Information.

U.S. Pat. No. 6,456,862 and U.S. Pat. No. 7,072,701, both assigned tothe assignee of the present application and both hereby incorporated byreference, disclose methods for spectrophotometric blood oxygenationmonitoring. Oxygen saturation within blood is defined as:

$\begin{matrix}{{O_{2}{saturation}\mspace{14mu} \%} = {\frac{{HbO}_{2}}{\left( {{HbO}_{2} + {Hb}} \right)}*100\%}} & \left( {{Eqn}.\mspace{11mu} 1} \right)\end{matrix}$

These methods, and others known within the prior art, utilize variantsof the Beer-Lambert law to account for optical attenuation in tissue ata particular wavelength. Relative concentrations of oxyhemoglobin (HbO₂)and deoxyhemoglobin (Hb), and therefore oxygenation levels, within atissue sample are determinable using changes in optical attenuation:

$\begin{matrix}{{\Delta \; A_{\lambda}} = {{- {\log \left( \frac{I_{t\; 2}}{I_{t\; 1}} \right)}_{\lambda}} = {\alpha_{\lambda}*\Delta \; C*d*B_{\lambda}}}} & \left( {{Eqn}.\mspace{11mu} 2} \right)\end{matrix}$

wherein “A_(λ)” represents the optical attenuation in tissue at aparticular wavelength λ (units: optical density or OD); “I” representsthe incident light intensity (units: W/cm²); “α_(λ)” represents thewavelength dependent absorption coefficient of the chromophore (units:OD*cm⁻¹*μM⁻¹); “C” represents the concentration of chromophore (units:μM); “d” represents the light source to detector (optode) separationdistance (units: cm); and “B_(λ)” represents the wavelength dependentlight scattering differential pathlength factor (unitless)

To non-invasively determine oxygen saturation within tissue accurately,it is necessary to account for the optical properties (e.g., absorptioncoefficients or optical densities) of the tissue being interrogated. Insome instances, the absorption coefficients or optical densities for thetissue components that create background light absorption and scatteringcan be assumed to be relatively constant over a selected wavelengthrange. The graph shown in FIG. 1, which includes tissue data plottedrelative to a Y-axis of values representative of absorption coefficientvalues and an X-axis of wavelength values, illustrates such an instance.The aforesaid constant value assumption is reasonable in a testpopulation where all of the subjects have approximately the same tissueoptical properties; e.g., skin pigmentation, muscle and bone density,etc. A tissue interrogation method that relies upon such an assumptionmay be described as being wavelength independent within the selectedwavelength range and subject independent. Our findings indicate that thesame assumption is not reasonable, however, in a population of subjectshaving a wide spectrum of tissue optical properties (e.g., a range ofsignificantly different skin pigmentations from very light to very dark)unless consideration for the wide spectrum of tissue optical propertiesis provided otherwise.

What is needed, therefore, is a method for non-invasively determiningthe level of oxygen saturation within biological tissue that accountsfor optical influences from the specific tissue through which the lightsignal passes.

DISCLOSURE OF THE INVENTION

According to one aspect of the present invention, a method and apparatusfor non-invasively determining the blood oxygen saturation level withina subject's tissue is provided. In one embodiment, the method includesthe steps of: 1) providing a near infrared spectrophotometric sensoroperable to transmit light along a plurality of wavelengths into thesubject's tissue; 2) sensing the light transmitted into the subject'stissue using the sensor, and producing signal data representative of thelight sensed from the subject's tissue; 3) processing the signal data,including accounting for physical characteristics of the subject; and 4)determining the blood oxygen saturation level within the subject'stissue using a difference in attenuation between the wavelengths.

The apparatus includes at least one sensor having at least one lightsource and at least one light detector, which sensor is operablyconnected to a processor. The light source is operable to transmit lightalong a plurality of wavelengths into the subject's tissue, and toproduce signal data representative of the light sensed from thesubject's tissue. The algorithm selectively produces calibrationconstants for use with the sensor that account for the specific physicalcharacteristics of the particular subject being sensed. The calibrationconstants are produced using the signal data.

According to another aspect of the present invention, a method forcalibrating a NIRS sensor is provided that includes the steps of: 1)transmitting light into a subject's tissue using the sensor; 2) sensingthe light using the sensor along a plurality of wavelengths after thelight travels through the subject's tissue, and producing signal datafrom the sensed light; and 3) calibrating the sensor using the signaldata.

The present method and apparatus provides advantageous accuracy. Allprior art non-invasive devices and methods for deter mining blood oxygensaturation level within a subject's tissue, of which we are aware, donot consider the specific physical characteristics of the particularsubject being sensed. The sensor is calibrated by use of assumedconstants and for relative to a source (e.g., a phantom sample,empirical data, etc.) other than the subject being sensed; i.e.,calibrated in a “subject independent” manner. The present device andmethod, in contrast, considers the specific physical characteristics(e.g., tissue pigment, muscle and bone density and mass, etc.) of theparticular subject by initially sensing the subject's tissue, creatingsignal data based on the sensing, and accounting for the specificphysical characteristics of the subject using the signal data. Thesensor, now calibrated in a “subject dependent” manner, can be useddetermine the tissue blood oxygen saturation level of the subjecttissue. As a result, the sensor is able to provide a more accurateassessment of the subject's blood oxygen saturation level within thetissue being sensed.

Another advantage of the present method and apparatus is that accurateblood oxygen saturation level information can be provided for apopulation of subjects having a wide range of physical characteristics.Physical characteristics (e.g., tissue pigmentation, thickness anddensity, etc.) naturally vary between subjects, and thosecharacteristics create differences in light attenuation, backgroundscattering and absorption. The present method and apparatus considersthe physical characteristics of the specific subject being tested, andcalibrates the sensor with signal data generated from sensing the tissueof the specific subject. Consequently, the present method and deviceaccounts for the differences in light attenuation specific to thatsubject and enables the tissue blood oxygenation saturation level ofsubjects having a wide range of physical characteristics to beaccurately sensed.

These and other objects, features, and advantages of the presentinvention method and apparatus will become apparent in light of thedetailed description of the invention provided below and theaccompanying drawings. The methodology and apparatus described belowconstitute a preferred embodiment of the underlying invention and donot, therefore, constitute all aspects of the invention that will or maybecome apparent by one of skill in the art after consideration of theinvention disclosed overall herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a graph diagrammatically illustrating tissue data plottedrelative to a Y-axis of values representative of absorption coefficientvalues, and an X-axis of wavelength values.

FIG. 2 is a diagrammatic representation of a NIRS sensor.

FIG. 3 is a diagrammatic representation of a NIRS sensor placed on asubject's head.

FIG. 4 is a diagrammatic view of a NIRS sensor.

FIG. 5 is a graph having values diagrammatically representative ofsubject-specific calibration coefficients plotted along a Y-axis, TOPindex values plotted along an X-axis, and data representative ofdeoxyhemoglobin values and oxyhemoglobin values plotted therebetweenwith best-fit curves applied thereto.

FIG. 6 is a flow chart illustrating steps according to one aspect of thepresent invention.

DETAILED DESCRIPTION THE INVENTION

The present method of and apparatus for non-invasively determining theblood oxygen saturation level within a subject's tissue is provided thatutilizes a near infrared spectrophotometric (NIRS) sensor that includesa transducer capable of transmitting a light signal into the tissue of asubject and sensing the light signal once it has passed through thetissue via transmittance or reflectance. The present method andapparatus can be used with a variety of NIRS sensors, and is nottherefore limited to any particular NIRS sensor.

Referring to FIGS. 2-4, an example of an acceptable NIRS sensor includesa transducer portion 10 and processor portion 12. The transducer portion10 includes an assembly housing 14 and a connector housing 16. Theassembly housing 14, which is a flexible structure that can be attacheddirectly to a subject's body, includes one or more light sources 18 andlight detectors 19, 20. A disposable adhesive envelope or pad ispreferably used for mounting the assembly housing 14 easily and securelyto the subject's skin. Light signals of known but different wavelengthsfrom the light sources emit through a prism assembly. The light sources18 are preferably laser diodes that emit light at a narrow spectralbandwidth at predetermined wavelengths. The laser diodes may be mountedremote from the assembly housing 14; e.g., in the connector housing 16or within the processor portion 12. In these embodiments, a fiber opticlight guide is optically interfaced with the laser diodes and the prismassembly that is disposed within the assembly housing 14. In otherembodiments, the light sources 18 are mounted within the assemblyhousing 14. A first connector cable 26 connects the assembly housing 14to the connector housing 16 and a second connector cable 28 connects theconnector housing 16 to the processor portion 12. The light detectors19, 20 each include one or more photodiodes. The photodiodes are alsooperably connected to the processor portion 12 via the first and secondconnector cables 26, 28. Other examples of acceptable NIRS sensors aredescribed in U.S. Patent Application No. 60/751,009 filed on Dec. 16,2005, and U.S. Patent Application No. 60/729,339 filed on Oct. 21, 2005,both of which applications are commonly assigned to the assignee of thepresent application and both of which are hereby incorporated byreference in their entirety.

The processor portion 12 includes a processor for processing lightintensity signals associated with the light sources 18 and the lightdetectors 19, 20 as described herein. A person of skill in the art willrecognize that the processor may assume various forms (e.g., digitalsignal processor, analog device, etc.) capable of performing thefunctions described herein. The processor utilizes an algorithm thatcharacterizes a change in attenuation as a function of the difference inattenuation between different wavelengths. The algorithm accounts forthe effects of pathlength and parameter “E”, which represents energylosses (“G”) due to light scattering within tissue, other backgroundabsorption losses (“F”) from biological compounds, and other unknownlosses (“N”) including measuring apparatus variability (E=G+F+N). Aswill be discussed below, the parameter “E” reflects energy losses notspecific to the subject being tested with a calibrated sensor (i.e.,“subject-independent”).

The absorption A_(bλ) detected from the deep light detector 20 includesattenuation and energy losses from both the deep and shallow tissue,while the absorption A_(xλ), detected from the shallow light detector 19includes attenuation and energy losses from shallow tissue. AbsorptionsA_(bλ) and A_(xλ) can be expressed in the form of Equation 3 andEquation 4:

$\begin{matrix}{A_{b\mspace{11mu} \lambda} = {{- {\log \left( \frac{I_{b}}{I_{o}} \right)}_{\lambda}} = {{\alpha_{\lambda}*C_{b}*L_{b}} + {\alpha_{\lambda}*C_{x}*L_{x}} + E_{\lambda}}}} & \left( {{Eqn}.\mspace{11mu} 3} \right) \\{A_{x\; \lambda} = {{- {\log \left( \frac{I_{x}}{I_{o}} \right)}_{\lambda}} = {{\alpha_{\lambda}*C_{x}*L_{x}} + E_{x\; \lambda}}}} & \left( {{Eqn}.\mspace{11mu} 4} \right)\end{matrix}$

In some applications (e.g., infants), a single light detector may beused, in which case Equation 5 is used:

A _(bλ)=−log(I _(b)/I_(o))_(λ)=α_(λ) *C _(b)*L_(b) +E _(λ)  (Eqn 5)

If both the deep and shallow detectors are used, then substitutingEquation 4 into Equation 3 yields A′_(λ), which represents attenuationand energy loss from deep tissue only:

A′ _(λ) −A _(bλ) −A _(xλ)=α_(λ) *C _(b) *I _(b)+(E _(λ) −E _(xλ9l ))  (Eqn. 6)

From Equation 5 or Equation 6, L is the effective pathlength of thephoton traveling through the deep tissue and A′₁ and A′₂ represent lightattenuation at two different wavelengths to determine differentialwavelength light attenuation ΔA′₁₂:

A′ ₁ −A′ ₂ =ΔA′ ₁₂   (Eqn. 7)

Substituting Equation 5 or 6 into Equation 7 for A′₁ and A′₂, ΔA′₁₂ canbe expressed as:

ΔA′ ₁₂=α_(λ12) *C _(b)*L_(b) +ΔE′ ₁₂   (Eqn. 8)

and Equation 8 can be rewritten in expanded form:

ΔA′ ₁₂

(α_(r1)−α_(r2))[Hb] _(b)+(α_(o1)−α_(o2))[HbO ₂]

L _(b)+(E′ ₁ −E′ ₂)=(Δα_(r12) *[Hb] _(b) *L _(b))+(Δα_(o12) *[HbO ₂ ]*L_(b))+ΔE′ ₁₂   (Eqn. 9)

where:

(Δα_(r12)*[Hb]_(b)*L_(b)) represents the attenuation attributable to Hb;and

(Δα_(o12)*[HbO₂]_(b)*L_(b)) represents the attenuation attributable toHbO₂; and ΔE′₁₂ represents energy losses due to light scattering withintissue, other background absorption losses from biological compounds,and other unknown losses including measuring apparatus variability.

The multivariate form of Equation 9 is used to determine [HbO₂]_(b) and[Hb]_(b) with three different wavelengths:

$\begin{matrix}{{\begin{bmatrix}{\Delta \; A_{12}^{\prime}} & {\Delta \; E_{12}^{\prime}} \\{\Delta \; A_{13}^{\prime}} & {\Delta \; E_{13}^{\prime}}\end{bmatrix}\left( L_{b} \right)^{- 1}} = {\begin{bmatrix}{\Delta \; \alpha_{r\; 12}} & {\Delta \; \alpha_{o\; 12}} \\{\Delta \; \alpha_{r\; 13}} & {\Delta \; \alpha_{013}}\end{bmatrix}\begin{bmatrix}\lbrack{Hb}\rbrack_{b} \\\left\lbrack {HbO}_{2} \right\rbrack_{b}\end{bmatrix}}} & \left( {{Eqn}.\mspace{11mu} 10} \right)\end{matrix}$

Rearranging and solving for [HbO₂]_(b) and [Hb]_(b), simplifying the Δαmatrix into [Δα′]:

$\begin{matrix}{{{\begin{bmatrix}{\Delta \; A_{12}^{\prime}} \\{\Delta \; A_{13}^{\prime}}\end{bmatrix}\left\lbrack {\Delta \; \alpha^{\prime}} \right\rbrack}^{- 1}\left( L_{b} \right)^{- 1}} - {{\begin{bmatrix}{\Delta \; E_{12}^{\prime}} \\{\Delta \; E_{13}^{\prime}}\end{bmatrix}\left\lbrack {\Delta \; \alpha^{\prime}} \right\rbrack}^{- 1}{\left( L_{b} \right)^{- 1}\begin{bmatrix}\lbrack{Hb}\rbrack_{b} \\\left\lbrack {HbO}_{2} \right\rbrack_{b}\end{bmatrix}}}} & \left( {{Eqn}.\mspace{11mu} 11} \right)\end{matrix}$

Then combined matrices [ΔA′] [Δα′]⁻¹=[A_(c)] and [ΔE] [Δα′]⁻¹=[Ψ_(c)]:

$\begin{matrix}{{{\begin{bmatrix}A_{Hb} \\A_{{HbO}_{2}}\end{bmatrix}\left( L_{b} \right)^{- 1}} - {\begin{bmatrix}\Psi_{Hb} \\\Psi_{{HbO}_{2}}\end{bmatrix}\left( L_{b} \right)^{- 1}}} = \begin{bmatrix}\lbrack{Hb}\rbrack_{b} \\\left\lbrack {HbO}_{2} \right\rbrack_{b}\end{bmatrix}} & \left( {{Eqn}.\mspace{11mu} 12} \right)\end{matrix}$

The parameters A_(Hb) and A_(HbO2) represent the product of the matrices[ΔA_(λ)] and [Δα′]⁻¹ and the parameters Ψ_(Hb) and Ψ_(HbO2) representthe product of the matrices [ΔE′_(λ)] and [Δα′]⁻¹. To determine thelevel of cerebral tissue blood oxygen saturation (SnO₂), Equation 12 isrearranged using the form of Equation 1 and is expressed as follows:

$\begin{matrix}{{{SnO}_{2}\%} = {\frac{\left( {A_{{HbO}_{2}} - \Psi_{{HbO}_{2}}} \right)}{\left( {A_{{HbO}_{2}} - \Psi_{{HbO}_{2}} + A_{Hb} - \Psi_{Hb}} \right)}*100\%}} & \left( {{Eqn}.\mspace{11mu} 13} \right)\end{matrix}$

Note that tissue blood oxygen saturation is sometimes symbolized asStO₂, SctO2, CrSO₂, or rSO₂. The effective pathlength L_(b) cancels outin the manipulation from Equation 12 to Equation 13.

The value for SnO₂ is initially determined from an empirical referenceof weighted combination of venous and arterial oxygen saturation (SmvO₂)value, for example using:

SmvO₂=Kv*SvO₂+Ka*SaO₂   (Eqn. 14),

and the empirically determined values for SvO₂ and SaO₂, where the team“SvO₂” represents venous oxygen saturation, the term “SaO₂” representsarterial oxygen saturation, and the terms Kv and Ka are the weightedvenous and arterial contributions respectively (Kv+Ka=1). Theempirically determined values for SvO₂ and SaO₂ are based on datadeveloped by discrete sampling or continuous monitoring of the subject'sblood performed at or about the same time as the sensing of the tissuewith the sensor; e.g., blood samples discretely collected can beanalyzed by blood gas analysis and blood samples continuously monitoredcan be analyzed using a fiber optic catheter inserted within a bloodvessel. The temporal and physical proximity of the NIRS sensing and thedevelopment of the empirical data helps assure accuracy. The initialvalues for Kv and Ka within Equation 14 are clinically reasonable valuesfor the circumstances at hand. The values for A_(HbO2) and A_(Hb) aredetermined mathematically using the values for I_(bλ) and I_(xλ) foreach wavelength sensed with the NIRS sensor (e.g., using Equation 3 & 4for deep and shallow detectors or Equation 5 for a single detector). Thecalibration parameters Ψ_(Hb) and Ψ_(HbO2), which account for energylosses due to scattering as well as other background absorption frombiological compounds, are then determined using Equation 14 andnon-linear regression techniques by correlation to different weightedvalues of SvO₂ and SaO₂; i.e., different values of Ka and Kv.Statistically acceptable values of Kv and Ka and Ψ_(Hb) and Ψ_(HbO2) areconverged upon using the non-linear regression techniques. Experimentalfindings show that with proper selection of Ka and Kv, the calibrationparameters 105 _(Hb) and Ψ_(HbO2) are constant within a statisticallyacceptable margin of error for an individual NIRS sensor used to monitorbrain oxygenation on different human subjects.

The above-identified process produces a NIRS sensor calibrated relativeto a particular subject using invasive techniques, or a NIRS sensorcalibrated relative to an already calibrated sensor (or relative to aphantom sample). When these calibrated sensors are used thereafter on adifferent subject, they do not account for the specific physicalcharacteristics of the particular subject being tested. The presentmethod and apparatus as described below permits a NIRS sensor to becalibrated in a non-invasive manner that accounts for specific physicalcharacteristics of the particular subject being sensed.

Certain physical characteristics will vary from subject to subject, suchas but not limited to, tissue pigmentation and thickness and density ofmuscle and/or bone. The present method and apparatus accounts forbackground tissue's wavelength dependent light attenuation differencesdue to these subject-dependent physical characteristics by sensing thesubject's tissue, creating signal data from the sensing, and using thesignal data to create one or more “subject-specific” calibrationconstants that account for the specific characteristics of the subject.For example, during an initial phase of monitoring, light is transmittedinto and sensed passing out of the subject's tissue. Signal datarepresentative of the sensed light is analyzed to account for thephysical characteristics of the subject, and one or moresubject-specific calibration constants indicative of the specificphysical characteristics are created. The subject-specific calibrationconstants are subsequently used to determine properties such as theblood oxygen saturation level, deoxyhemoglobin concentration,oxyhemoglobin concentration, etc.

The subject-specific calibration constants can be determined by usingthe sensed signal data to create a tissue optical property (TOP) indexvalue. The TOP index value is derived from wavelength dependent lightattenuation attributable to physical characteristics such as tissuepigmentation, thickness and density of tissue, etc. These physicalcharacteristics are collectively considered in determining the TOP indexvalue because the characteristics have absorption coefficients thatincrease with decreasing wavelength from the near-infrared region to thered region (i.e., from about 900 nm to about 400 nm) mainly due to thepresence of melanin, the light absorbing pigmentation in skin andtissue. For example, it has been reported by S. L. Jacques et al., thatlight absorption in skin due to melanin can be described by therelationship: μ_(a)=1.70×10¹² (wavelength in nm)^(−3.48) [cm⁻¹] in thewavelength range from about 400 nm to about 850 nm. If the overall lightabsorption characteristics of tissue are modeled to follow that ofmelanin, then the TOP light absorption coefficients (α_(TOP)) can bedetermined using the same equation for the particular wavelengths oflight used in the interrogation of the tissue (where A=1.7×10¹² andT=−3.48):

α_(TOP)=A*(wavelength)^(−T)   (Eqn. 15)

To determine the TOP index value, one or more of the wavelengths in thenear-infrared region to the red region (i.e., from about 900 nm to about600 nm; e.g., 690 nm, 780 nm, 805 nm, 850 nm) are sensed. Redwavelengths are favored because red light is more sensitive to thetissue optical properties than infrared light. Lower wavelengths oflight could also be used, but suffer from increased attenuation from thehigher tissue and hemoglobin absorption coefficients, resulting inreduced tissue penetration, reduced detected light signal strength, andresultant poor signal to noise ratio.

To calculate the TOP index value (identified in Equation 16 as “TOP”), afour wavelength, three unknown differential attenuation algorithm(following similarly to the derivation shown by Equations 3-10), is usedsuch as that shown in Equation 16:

$\begin{matrix}{\begin{bmatrix}{\Delta \; A_{12}^{\prime}} \\{\Delta \; A_{13}^{\prime}} \\{\Delta \; A_{14}^{\prime}}\end{bmatrix}{{\left( L_{b} \right)^{- 1}\begin{bmatrix}{\Delta\alpha}_{r\; 12}^{\prime} & {\Delta\alpha}_{o\; 12}^{\prime} & {\Delta\alpha}_{{TOP}\; 12}^{\prime} \\{\Delta\alpha}_{r\; 13}^{\prime} & {\Delta\alpha}_{o\; 13}^{\prime} & {\Delta\alpha}_{{TOP}\; 13}^{\prime} \\{\Delta\alpha}_{r\; 14}^{\prime} & {\Delta\alpha}_{o\; 14}^{\prime} & {\Delta\alpha}_{{TOP}\; 14}^{\prime}\end{bmatrix}}\begin{bmatrix}{Hb} \\{HbO}_{2} \\{TOP}\end{bmatrix}}} & \left( {{Eqn}.\mspace{11mu} 16} \right)\end{matrix}$

Alternatively, Equation 17 shown below could be used. Equation 17accounts for energy losses “E” as described above:

$\begin{matrix}{\begin{bmatrix}{\Delta \; A_{12}^{\prime}} & {\Delta \; E_{12}^{\prime}} \\{\Delta \; A_{13}^{\prime}} & {\Delta \; E_{13}^{\prime}} \\{\Delta \; A_{14}^{\prime}} & {\Delta \; E_{14}^{\prime}}\end{bmatrix}{{\left( L_{b} \right)^{- 1}\begin{bmatrix}{\Delta\alpha}_{r\; 12}^{\prime} & {\Delta\alpha}_{o\; 12}^{\prime} & {\Delta\alpha}_{{TOP}\; 12}^{\prime} \\{\Delta\alpha}_{r\; 13}^{\prime} & {\Delta\alpha}_{o\; 13}^{\prime} & {\Delta\alpha}_{{TOP}\; 13}^{\prime} \\{\Delta\alpha}_{r\; 14}^{\prime} & {\Delta\alpha}_{o\; 14}^{\prime} & {\Delta\alpha}_{{TOP}\; 14}^{\prime}\end{bmatrix}}\begin{bmatrix}{Hb} \\{HbO}_{2} \\{TOP}\end{bmatrix}}} & \left( {{Eqn}.\mspace{11mu} 17} \right)\end{matrix}$

The TOP index value determinable from Equations 16 or 17 accounts forsubject tissue optical properties variability and can be converted to a“corrective” factor used to determine accurate tissue blood oxygensaturation SnO₂. In some embodiments, the TOP index value can be usedwith a database to determine subject-specific calibration constants(e.g., Z_(Hb) and Z_(HbO2)). The database contains data, at least someof which is empirically collected, pertaining to oxyhemoglobin anddeoxyhemoglobin concentrations for a plurality of subjects. Theconcentration data is organized relative to a range of TOP index valuesin a manner that enables the determination of the subject-specificcalibration constants. The organization of the information within thedatabase can be accomplished in a variety of different ways.

For example, the empirical database may be organized in the form of agraph having subject-specific calibration coefficients plotted along they-axis versus TOP index values plotted along the x-axis. An example ofsuch a graph is shown in FIG. 5, which contains data 30 representing thedifferences between calculated deoxyhemoglobin values (Hb) values andempirically derived deoxyhemoglobin values (the differences referred toin FIG. 5 as “Hb-offset2 data”), and a best fit curve 32 applied to aportion of that data 30. The graph also contains data 34 representingthe differences between calculated oxyhemoglobin values (HbO2) valuesand empirically derived oxyhemoglobin values (the differences referredto in FIG. 5 as “HbO2-offset2 data”), and another best-fit curve 36applied to a portion of that data 34. In the example shown in FIG. 5, astatistically significant number of the data 30, 34 for each curve lieswithin the sloped portion 32 a, 36 a (i.e., the portion that does nothave a constant calibration constant value). At each end of the slopedportion 32 a, 36 a, the curves 32, 36 are depicted as having constantcalibration values 32 b, 32 c, 36 b, 36 c for convenience sake. Thevalues for the subject-specific calibration coefficients Z_(Hb) andZ_(HbO2) are determined by drawing a line (e.g., see phantom line 38)perpendicular to the TOP index value axis at the determined TOP indexvalue. The subject-specific calibration constant (Z_(Hb)) fordeoxyhemoglobin is equal to the value on the calibration constant axisaligned with the intersection point between the perpendicular line andthe “Hb-offset2” curve, and the subject-specific calibration constant(Z_(HbO2)) for oxyhemoglobin is equal to the value on the calibrationconstant axis aligned with the intersection point with the“HbO2-offset2” curve.

Alternatively, the subject-specific calibration constant values may bedetermined using an empirical database in a form other than a graph. Forexample, a mathematical solution can be implemented rather than theabove-described graph. The mathematical solution may use linearequations representing the “Hb-offset2” and the “HbO2-offset2” curves.

Once the subject-specific calibration constant values are determined,they are utilized with a variation of Equation 13:

$\begin{matrix}{{{SnO}_{2}\%} = {\frac{\left( {A_{{HbO}_{2}} - \Psi_{{HbO}_{2}} + Z_{{HbO}_{2}}} \right)}{\left( {A_{{HbO}_{2}} - \Psi_{{HbO}_{2}} + Z_{{HbO}_{2}} + A_{Hb} - \Psi_{Hb} + Z_{Hb}} \right)}*100\%}} & \left( {{Eqn}.\mspace{11mu} 18} \right)\end{matrix}$

to determine the cerebral blood oxygen saturation level.

The above-described process for determining the subject-specificcalibration constants can be performed one or more times in the initialperiod of sensing the subject to calibrate the sensor to that particularsubject, preferably right after the sensor is attached to the subject.The subject-dependent calibration constants can then be used with analgorithm for measurement of a subject's blood oxygen saturation levelusing the same or different signal data. The algorithm in which thesubject-dependent calibration constants are utilized may be the samealgorithm as used to determine the constants, or a different algorithmfor determining the tissue oxygen saturation level. For example,calibration constants can be used with the three wavelength methoddisclosed above in Equations 2-14, and in U.S. Pat. No. 6,456,862, whichis hereby incorporated by reference. Prior to the cerebral blood oxygensaturation level being calculated, the subject-specific calibrationconstants Z_(Hb) and Z_(HbO2) can be incorporated as corrective factorsinto the three wavelength algorithm (e.g., incorporated into Eqn. 13).As a result, a more accurate determination of the subject's tissueoxygen saturation level is possible. FIG. 6 illustrates the abovedescribed steps within a flow chart.

In alternative embodiments, the TOP index methodology disclosed abovecan be used within an algorithm in a subject-independent manner. Thisapproach does not provide all of the advantages of the above describedsubject—dependent methodology and apparatus, but does provide improvedaccuracy by specifically accounting for subject skin pigmentation. Forexample, the TOP absorption coefficients can be determined as describedabove and utilized within Equation 16 or Equation 17. Regardless of theequation used, the determined values for deoxyhemoglobin (Hb) andoxyhemoglobin (HbO₂) can subsequently be used to determine the tissueoxygen saturation level. For example, the Hb and HbO₂ values can beutilized within Equations 11 through 13.

Although the present method and apparatus are described above in termsof sensing blood oxygenation within cerebral tissue, the present methodand apparatus are not limited to cerebral applications and can be usedto determine tissue blood oxygenation saturation within tissue foundelsewhere within the subject's body. If the present invention isutilized to determine the tissue blood oxygenation saturation percentageis typically symbolized as StO₂ or rSO₂.

Since many changes and variations of the disclosed embodiment of theinvention may be made without departing from the inventive concept, itis not intended to limit the invention otherwise than as required by theappended claims.

What is claimed is:
 1. (canceled)
 2. A method for non-invasivelydetermining a blood oxygen parameter value of a subject's tissue,comprising: providing a spectrophotometric sensor having one or moretransducers and a processor portion, each transducer including at leastone light source and at least one light detector, and the processorportion including at least one processor; using the at least oneprocessor to execute algorithmic instructions stored in a memory device,which instructions cause the at least one processor to: control the atleast one light source to transmit light at one or more wavelengths ofred light into the subject's tissue; control the at least one lightdetector to sense the transmitted red light after passage through thesubject's tissue and produce initial signals representative of thesensed red light; receive and process the initial signals from the atleast one light detector and produce first signal data from the initialsignals; selectively alter the algorithmic instructions using the firstsignal data to account for the specific physical characteristics of theparticular subject's tissue being sensed; and determine the blood oxygenparameter value within the subject's tissue using the selectivelyaltered algorithmic instructions.
 2. The method of claim 1, wherein theone or more wavelengths of red light include light wavelengths in therange of about 622 nm to 780 nm.
 3. The method of claim 2, wherein theone or more wavelengths of red light include visible light wavelengthsin the range of about 622 nm to 700 nm.
 4. The method of claim 3,wherein the one or more wavelengths of red light include light at thewavelength of about 690 nm.
 5. The method of claim 1, wherein the bloodoxygen parameter value within the subject's tissue is determined usingsignals representative of light sensed by the at least one lightdetector subsequent to the sensing of the light used to produce theinitial signals.
 6. The method of claim 1, wherein the first signal datais attributable to non-pulsatile blood flow within the subject's tissue.7. The method of claim 1, wherein the blood oxygen parameter isoxyhemoglobin.
 8. The method of claim 1, wherein the blood oxygenparameter is deoxyhemoglobin.
 9. An apparatus for non-invasivelydetermining a blood oxygen parameter value of a subject's tissue,comprising: at least one transducer having at least one light source andat least one light detector, wherein the transducer is configured to beapplied to a tissue surface of the subject with the at least one lightsource positioned to transmit light into the subject's tissue and the atleast one light detector positioned to sense the transmitted light afterpassage through the subject's tissue; and a processor portion having atleast one processor in communication with a memory device storingalgorithmic instructions, which instructions cause the at least oneprocessor to selectively: control the at least one light source totransmit light at one or more wavelengths of red light into thesubject's tissue; control the at least one light detector to sense thetransmitted red light after passage through the subject's tissue andproduce initial signals representative of the sensed red light; receiveand process the initial signals from the at least one light detector andproduce first signal data from the initial signals; alter thealgorithmic instructions using the first signal data to account for thespecific physical characteristics of the particular subject's tissuebeing sensed; and determine the blood oxygen parameter value within thesubject's tissue using the selectively altered algorithmic instructions.10. The apparatus of claim 9, wherein the one or more wavelengths of redlight include light wavelengths in the range of about 622 nm to 780 nm.11. The apparatus of claim 10, wherein the one or more wavelengths ofred light include visible light wavelengths in the range of about 622 nmto 700 nm.
 12. The apparatus of claim 11, wherein the one or morewavelengths of red light include light at the wavelength of about 690nm.
 13. The apparatus of claim 9, wherein the blood oxygen parametervalue within the subject's tissue is determined using signalsrepresentative of light sensed by the at least one light detectorsubsequent to the sensing of the light used to produce the initialsignals.
 14. The apparatus of claim 9, wherein the blood oxygenparameter is oxyhemoglobin.
 15. The method of claim 9, wherein the bloodoxygen parameter is deoxyhemoglobin.
 16. The apparatus of claim 9,wherein the at least one light source is configured to transmit lightalong a plurality of wavelengths into the subject's tissue.